Tracking and data association
CONDENSATION—Conditional Density Propagation forVisual Tracking
International Journal of Computer Vision
MCMC-Based Particle Filtering for Tracking a Variable Number of Interacting Targets
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Rao-Blackwellized particle filter for multiple target tracking
Information Fusion
Visually tracking football games based on TV broadcasts
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Robust visual tracking for multiple targets
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part IV
A tutorial on particle filters for online nonlinear/non-GaussianBayesian tracking
IEEE Transactions on Signal Processing
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We propose a novel efficient algorithm for robust tracking of a fixed number of targets in real-time with low failure rate. The method is an instance of Sequential Importance Resampling filters approximating the posterior of complete target configurations as a mixture of Gaussians. Using predicted target positions by Kalman filters, data associations are sampled for each measurement sweep according to their likelihood allowing to constrain the number of associations per target. Updated target configurations are weighted for resampling pursuant to their explanatory power for former positions and measurements. Fixed-lag of the resulting positions increases the tracking quality while smart resampling and memoization decrease the computational demand. We present both, qualitative and quantitative experimental results on two demanding real-world applications with occluded and highly confusable targets, demonstrating the robustness and real-time performance of our approach outperforming current state-of-the-art.